Twitter Population Sample Bias and its impact on predictive outcomes

This is the authors' abstract. We don't add key points for this paper.

In the past years a lot of effort has been spent analyzing online social network data to understand how the world reality is reflected in the "virtual" world. Twitter is by far the network most used in these studies, given its policy of public data availability. However, a big discussion is still on on whether the data available is enough to make user characterization or event outcomes prediction, and what are the pitfalls people do not usually account for. In this direction, we propose a new methodology for drawing representative samples from Twitter data, which is divided into four phases: (i) user filtering, (ii) user demographic characterization, (iii) user sampling, and (iv) event prediction. The methodology is tested into a common scenario in Twitter event outcome prediction: elections. The methodology was tested with municipality elections from six different Brazilian cities, and compared to official election results. Results show it is worth further investigating the topic, but that a very hight number of messages is required to match real data distributions.